---
id: metrics-tool-use
title: Tool Use
sidebar_label: Tool Use
---

<head>
  <link
    rel="canonical"
    href="https://deepeval.com/docs/metrics-topic-adherence"
  />
</head>

import Equation from "@site/src/components/Equation";
import MetricTagsDisplayer from "@site/src/components/MetricTagsDisplayer";

<MetricTagsDisplayer usesLLMs={true} multiTurn={true} agent={true} referenceless={true} />

The Tool Use metric is a multi-turn agentic metric that evaluates whether your LLM agent's **tool selection and argument generation** capablilities. It is a self-explaining eval, which means it outputs a reason for its metric score.

## Required Arguments

To use the `ToolUseMetric`, you'll have to provide the following arguments when creating a [`ConversationalTestCase`](https://www.deepeval.com/docs/evaluation-multiturn-test-cases):

- `turns`

You can learn more about how it is calculated [here](#how-is-it-calculated).

## Usage

The `ToolUseMetric()` can be used for [end-to-end](/docs/evaluation-end-to-end-llm-evals) multi-turn evaluations of agents.

```python
from deepeval import evaluate
from deepeval.metrics import ToolUseMetric
from deepeval.test_case import Turn, ConversationalTestCase, ToolCall

convo_test_case = ConversationalTestCase(
    turns=[
        Turn(role="...", content="..."), 
        Turn(role="...", content="...", tools_called=[...])
    ],
)
metric = ToolUseMetric(threshold=0.5)

# To run metric as a standalone
# metric.measure(convo_test_case)
# print(metric.score, metric.reason)

evaluate(test_cases=[convo_test_case], metrics=[metric])
```

There is **ONE** mandatory and **SIX** optional parameters when creating a `ToolUseMetric`:

- `available_tools`: a list of `ToolCall`s that give context on all the tools that were available to your LLM agent. This list is used to evaluate your agent's tool selection capability.
- [Optional] `threshold`: a float representing the minimum passing threshold, defaulted to 0.5.
- [Optional] `model`: a string specifying which of OpenAI's GPT models to use, **OR** [any custom LLM model](/docs/metrics-introduction#using-a-custom-llm) of type `DeepEvalBaseLLM`. Defaulted to 'gpt-4o'.
- [Optional] `include_reason`: a boolean which when set to `True`, will include a reason for its evaluation score. Defaulted to `True`.
- [Optional] `strict_mode`: a boolean which when set to `True`, enforces a binary metric score: 1 for perfection, 0 otherwise. It also overrides the current threshold and sets it to 1. Defaulted to `False`.
- [Optional] `async_mode`: a boolean which when set to `True`, enables [concurrent execution within the `measure()` method.](/docs/metrics-introduction#measuring-a-metric-in-async) Defaulted to `True`.
- [Optional] `verbose_mode`: a boolean which when set to `True`, prints the intermediate steps used to calculate said metric to the console, as outlined in the [How Is It Calculated](#how-is-it-calculated) section. Defaulted to `False`.

### As a standalone

You can also run the `ToolUseMetric` on a single test case as a standalone, one-off execution.

```python
...

metric.measure(convo_test_case)
print(metric.score, metric.reason)
```

:::caution
This is great for debugging or if you wish to build your own evaluation pipeline, but you will **NOT** get the benefits (testing reports) and all the optimizations (speed, caching, computation) the `evaluate()` function or `deepeval test run` offers.
:::

## How Is It Calculated

The `ToolUseMetric` score is determined through the following process:

1. Compute the **Tool Selection Score** for each unit interaction.
2. Compute the **Argument Correctness Score** for all unit interactions that include tool calls.

<Equation formula="\text{Tool Use Score} = \min(\text{ToolSelectionScore}, \text{ArgumentCorrectnessScore})" />

- The **Tool Selection Score** evaluates whether the agent chose the most appropriate tool for the task among all the available tools.
- The **Argument Correctness Score** assesses whether the arguments provided in the tool call were accurate and suitable for the task. This score is only considered when a tool call has been made.
